def import_data(directory,
channels,
tcns=None,
sl=slice(None),
check=True,
stage=1):
"""Import a channel's or channels' dataframe(s), cleaned of outliers. Optionally filter by
list of tcns and slice. Set check to False to skip 'view outliers' prompt
Input:
----------
directory : str
path to HDF5 store
channels : str or list
channels for which to return data. If string, returns DataFrame rather than dictionary
tcns : list
list of tcns to include. tcns missing data will automatically be dropped
sl : slice object
slice for rows.
check : bool
If false, bypass checking and drop any outliers found by clean_outliers()
Returns:
----------
time : Series
corresponding time channel
clean : dict (or DataFrame)
cleaned data ready for use.
"""
return_single = False
if isinstance(channels, str):
channels = [channels]
return_single = True
time, fulldata = ob.openHDF5(os.fspath(directory), channels)
t = time[sl]
clean = {}
for channel in channels:
raw = fulldata[channel][sl]
if tcns is not None:
raw = raw.loc[:, tcns].dropna(how='all', axis=1)
if check:
clean[channel] = check_and_clean(raw, stage)
else:
clean[channel] = clean_outliers(raw, stage)
if return_single:
return t, clean[channel]
else:
return t, clean
@author: giguerf
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PMG.COM.openbook import openHDF5
import PMG.COM.data as data
from PMG.COM.data import check_and_clean
import PMG.COM.plotstyle as style
import PMG.COM.table as tb
THOR = os.fspath('P:/AHEC/Data/THOR/')
chlist = ['11CHSTLEUPTHDSXB', '11CHSTRIUPTHDSXB', '11CHSTRILOTHDSXB', '11CHSTLELOTHDSXB']
time, fulldata = openHDF5(THOR, chlist)
for ch in chlist:
print(ch)
fulldata[ch] = check_and_clean(fulldata[ch],1)
table = tb.get('THOR')
table = table[table.TYPE.isin(['Frontale/Véhicule']) & table.VITESSE.isin([48,56])]
ok, slip = tb.tcns(tb.split(table, column='CBL_BELT', categories=['OK','SLIP']))
#%%
if 0:
cd = style.colordict(chlist)
titles = dict(zip(chlist, ['Upper Left', 'Upper Right', 'Lower Right', 'Lower Left']))
plt.close('all')
import matplotlib.pyplot as plt
from PMG.COM.openbook import openHDF5
from PMG.COM import table as tb
from matplotlib.animation import FuncAnimation
from matplotlib.widgets import Slider
import matplotlib.gridspec as gridspec
table = tb.get('SLED')
SLED = 'P:/SLED/Data/'
chlist = [
'12HEAD0000Y7ACXA', '12HEAD0000Y2ACXA', '12CHST0000Y7ACXC',
'12CHST0000Y2ACXC', '12PELV0000Y7ACXA', '12PELV0000Y2ACXA'
]
time, fulldata = openHDF5(SLED, chlist)
raw = fulldata['12PELV0000Y7ACXA'].iloc[:, 0]
#%% Animation
fig, ax = plt.subplots(figsize=(5, 3))
#ax.set(xlim=(-3, 3), ylim=(-1, 1))
ax.plot(raw, 'k')
line = ax.plot(raw, color='b', lw=2)[0]
text = ax.text(0.1, 0.1, '0', transform=ax.transAxes)
def run_animation():
anim_running = True
"""
Created on Mon May 14 16:04:21 2018
@author: tangk
"""
from PMG.COM.openbook import openHDF5
from PMG.COM import arrange
import pandas as pd
from PMG.COM.get_props import *
import matplotlib.pyplot as plt
from PMG.COM.plotfuns import *
cutoff = range(1600)
directory = 'P:\\Rear Impact\\Data\\'
t,fulldata = openHDF5(directory)
chdata = arrange.test_ch_from_chdict(fulldata,cutoff)
t = t.get_values()[cutoff]
def ch_rename(name):
return name[0:8] + name[12:15]
chdata = chdata.rename(columns=ch_rename)
iarv = pd.DataFrame([[60,180,700,73,193,779,87,154,390,73,193,779]],columns=chdata.columns)
peaks = arrange.arrange_by_peak(chdata.applymap(peakval))
p_iarv = pd.DataFrame(index=chdata.index,columns=chdata.columns)
for f in chdata.index:
for ch in chdata.columns:
p_iarv.set_value(f,ch,peaks[ch]['+tive'][f]/iarv[ch][0])